Automatic horizon picking in 3D seismic data using optical filters and minimum spanning tree (patent pending)
Why this work is in the frame
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Bibliographic record
Abstract
Horizon picking in 3D seismic data is a very challenging problem. The difficulty for automatic horizon extraction exists at least in two fold: (1) the selection of picks in a trace usually ignores lateral continuity, and (2) the trace traversal order can result in significantly different horizons so that the resulting picks in the same horizon often conflict with each other. In this paper, a pattern recognition‐based algorithm is presented to explicitly address these two difficulties: (1) select a pick within a trace by considering context information through orientation filters which help to preserve the lateral continuity among traces; (2) perform the trace selection using the minimum‐spanning tree (MST) algorithm based on the confidence maximum at each pick. Combining the pick selection and trace selection components together allows us to obtain highly accurate horizon surfaces.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it